Systems, methods, and computer programs for imaging an object and generating a measure of authenticity of the object

ABSTRACT

An imaging system ( 200 ) for imaging and generating a measure of authenticity of an object ( 10 ) comprises a dispersive imaging arrangement ( 30 ) and an image sensor arrangement ( 60 ). They are positioned so that, when electromagnetic radiation ( 20 ) from the object ( 10 ) illuminates the dispersive imaging arrangement ( 30 ), the radiation splits out in different directions into at least a non-dispersed part ( 40 ) and a dispersed part ( 50 ), and those are imaged by the image sensor arrangement ( 60 ). The imaging system ( 200 ) is configured to then generate a measure of authenticity of the object ( 10 ) depending at least on a relation between the imaged dispersed part, the imaged non-dispersed part, and reference spectral information. The invention also relates to imaging methods, computer programs, computer program products, and storage mediums.

TECHNICAL FIELD

The present invention relates to imaging systems for imaging an objectand generating a measure of authenticity of the object. The inventionalso relates to methods, computer programs, computer program products,and storage mediums for the same purposes.

BACKGROUND

The supply of counterfeit goods in a particular market causes a loss ofrevenue to manufacturers of the corresponding genuine goods, as well asto governments when those goods are subject to taxation. End users areadversely affected by counterfeit goods because they are supplied withproducts of inferior quality, which may even be dangerous to the healthof the end user for certain products, such as when medicines are thesubject of counterfeiting. The manufacturer of high-quality genuineproducts will consequently suffer a loss to its goodwill.

A number of anti-counterfeiting measures have been proposed in the priorart with respect, for example, to alcoholic and non-alcoholic drinks(beer, wine, liquor, soft-drinks, etc.), tobacco products (cigarettes,cigars, loose tobacco, etc.), medicinal products, perfumes and excisableproducts generally. It is known to make use of sophisticated printingtechniques to make the design on the package as hard to replicate aspossible.

It is also known to make use of fluorescing items that look one wayunder ambient light and look a different way under ultraviolet (UV)radiation. Also used are holographic images of varying degrees ofcomplexity. Other known techniques include watermark technology,engraved gravure lines and marks that change colour depending on heatapplied to the mark.

CN 202533362 U relates to a printed matter authenticity identificationdevice based on a multispectral imaging technology. The device comprisesa multispectral imager for carrying out multispectral scanning on a testsample (the multispectral imager comprising a light source, a grating,and an imaging detector), a spectral data processor for comparingspectral data obtained from scanning with spectral data of a standardsample, and a data server used for storing the spectral data of thestandard sample. If the difference between the spectral data obtainedfrom scanning and the spectral data of a standard sample exceeds a setthreshold value, the test sample is judged as fake. Otherwise, it isjudged as authentic.

The prior art also includes various imaging spectrometers used forscientific observations. These systems typically aim at obtaininghigh-resolution spatial and spectral information about all regions of ascene or object. In particular, imaging spectrometers are imagers thatallow extraction of three-dimensional spectral irradiance map of aplanar object (spatial-spectral data cube) I(x, y, A) by use oftwo-dimensional array detectors such as CCD (i.e., charge-coupleddevice) or CMOS (i.e., complementary metal-oxide-semiconductor) sensors.One dimension is the wavelength and the other two comprise the spatialinformation.

Two major categories of spectral imagers exist: the spectral scanningimagers and the snapshot spectral imagers. A review of multi- andhyperspectral imager can be found for example in Hagen et al, “Snapshotadvantage: a review of the light collection improvement for parallelhigh-dimensional measurement systems”, Optical Engineering 51(11),111702 (2012), and Hagen et al, “Review of snapshot spectral imagingtechnologies”, Optical Engineering 52(9), 090901 (September 2013).

One way to acquire three-dimensional information by a two-dimensionalsensor is to acquire sequentially images through mechanically scannedwheel or array of optical filters installed in front of an imager.Another possibility is to tune the central transmission band of a filtersuch as a multi-stage liquid crystal filter, an acousto-optic filter, ora Fabry-Perot interferometer. These two examples fall into the categoryof spectral scanning imagers.

Snapshot spectral imagers capable of simultaneous acquisition of imagesin different spectral bands through an array of filters exist and anexample is the multi-aperture filtered camera (MAFC), using lensletarrays with focal plane detector.

Transmission diffraction gratings based snapshot spectral imagingsystems also exist. An example is the computed tomography imagingspectrometer (CTIS) which either uses several crossed transmissiongratings or specifically designed Kinoform grating able to disperseseveral spectral orders around a zero order. Computed tomographyalgorithms have to be used to reconstruct the spectral radiance of theobject.

Another example with transmission diffraction grating is the codedaperture snapshot spectral imager (CASSI) which uses complex masks toshadow some parts of the image of the object in order to facilitate thespectra extraction.

Integral field imaging spectrometers rely also on diffraction gratingsto disperse the light. In these setups, the image is sliced by differentmethods to fit onto an input slit of a conventional spectrometer toextract spectra. Image slicing can be obtained either by use of fiberbundle and distributing individual fibers into an entrance slit, or byaperture division using lenslet array.

Fourier transform imaging spectrometers also exist in a separatecategory. An interferometer is scanned to obtain images at differentoptical path differences and spectra are reconstructed by Fouriertransform. Some setups rely on lenslet array to do aperture division andanalyse the average spectra at different parts of the image/object. Anexample is the multiple-image Fourier transform spectrometer (MIFTS)based on a Michelson interferometer. Another distinct example is thesnapshot hyperspectral imaging Fourier transform spectrometer (SHIFT)which uses pair of birefringent prisms to obtain different optical pathlengths.

In view of the above, there is a need for providing fast, simple,inexpensive, compact, and robust equipment for authentication purposes,in particular, but not only, for incorporation into hand-held auditdevices.

SUMMARY

To meet or at least partially meet the above-mentioned goals, imagingsystems, imaging methods, computer programs, computer program products,and storage mediums according to the invention are defined in theindependent claims. Particular embodiments are defined in the dependentclaims.

In one embodiment, an imaging system is provided for imaging an objectand generating a measure of authenticity of the object. The imagingsystem comprises one or more image sensors, the one or more imagesensors being hereinafter referred to as “image sensor arrangement”, andone or more optical elements, the one or more optical elements beinghereinafter referred to as “dispersive imaging arrangement”. Thedispersive imaging arrangement is so that, when electromagneticradiation from the object illuminates the dispersive imagingarrangement, at least part of the electromagnetic radiation splits outin different directions into at least a non-dispersed part and adispersed part. Furthermore, the dispersive imaging arrangement ispositioned relative to the image sensor arrangement in such a manner asto allow the image sensor arrangement to image said non-dispersed partin a first portion of the image sensor arrangement and said dispersedpart in a second portion thereof. The imaging system is configured for,after the image sensor arrangement has imaged the non-dispersed part anddispersed part in at least one imaging period, generating a measure ofauthenticity of the object depending at least on a relation between theimaged dispersed part, the imaged non-dispersed part, and referencespectral information.

Such an imaging system enables the efficient verification of whether,and/or the extent to which, the relation between the imaged dispersedpart, the imaged non-dispersed part, and reference spectral information,which represents the expected spectral composition of theelectromagnetic radiation from the object, matches the predictedphysics. If so, the object is likely to be authentic. Otherwise, theobject is more likely to be a counterfeit.

The invention also relates, in one embodiment, to an imaging method forimaging an object and generating a measure of authenticity of theobject. The imaging method makes use of: one or more image sensors, theone or more image sensors being referred to, as mentioned above, as“image sensor arrangement”, and one or more optical elements, the one ormore optical elements being referred to, as mentioned above, as“dispersive imaging arrangement”. The dispersive imaging arrangement isso that, when electromagnetic radiation from the object illuminates thedispersive imaging arrangement, at least part of the electromagneticradiation splits out in different directions into at least anon-dispersed part and a dispersed part. Furthermore, the dispersiveimaging arrangement is positioned relative to the image sensorarrangement in such a manner as to allow the image sensor arrangement toimage said non-dispersed part in a first portion of the image sensorarrangement and said dispersed part in a second portion thereof. Theimaging method comprises: imaging, by the image sensor arrangement, thenon-dispersed part and dispersed part in at least one imaging period,and generating a measure of authenticity of the object depending atleast on a relation between the imaged dispersed part, the imagednon-dispersed part, and reference spectral information.

The invention also relates, in some embodiments, to a computer programor a set of computer programs for carrying out an imaging method asdescribed above, to a computer program product or a set of computerprogram products for storing a computer program or a set of computerprograms as described above, and to a storage medium for storing acomputer program or a set of computer programs as described above.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention shall now be described, inconjunction with the appended figures, in which:

FIG. 1 schematically illustrates an object to be imaged and an imagingsystem in one embodiment of the invention;

FIG. 2 schematically illustrates an object to be imaged and a system inone embodiment of the invention, wherein the system comprises both animaging system and an illumination arrangement;

FIG. 3 schematically illustrates an object to be imaged and a system inone embodiment of the invention, wherein the system notably comprisesillumination elements arranged around a dispersive imaging arrangement;

FIGS. 4 to 6 schematically illustrate three imaging systems and objectsto be imaged, in three embodiments of the invention respectively;

FIGS. 7 and 8 schematically represent, using a thin lens-gratingapproximation, two imaging systems and marks to be imaged, in twoembodiments of the invention respectively, wherein FIG. 8 especiallyillustrates the order separation;

FIG. 9a schematically illustrates an imaging system in one embodiment ofthe invention, wherein the imaging system is an imaging device;

FIG. 9b schematically illustrates a system in one embodiment of theinvention, wherein the system comprises both an imaging system and anillumination arrangement, and wherein the system is an imaging device;

FIG. 10a schematically illustrates an imaging system in one embodimentof the invention, wherein the imaging system comprises an imaging deviceand said imaging device comprises an image sensor arrangement and adispersive imaging arrangement, but said imaging device is notconfigured to actually generate the measure of authenticity;

FIG. 10b schematically illustrates a system in one embodiment of theinvention, wherein the system comprises an imaging device and saidimaging device comprises an image sensor arrangement, a dispersiveimaging arrangement and an illumination arrangement, but said imagingdevice is not configured to actually generate the measure ofauthenticity;

FIG. 11 is a flowchart of an imaging method in one embodiment of theinvention;

FIGS. 12a to 12c are flowcharts of imaging methods in three embodimentsof the invention, wherein generating the measure of authenticity dependsat least on the extent to which the imaged dispersed part corresponds toa convolution of the imaged non-dispersed part and reference spectralinformation;

FIG. 13 is a flowchart of an imaging method in one embodiment of theinvention, involving the decoding of a code from a marking within theimaged non-dispersed part and verifying the authenticity of the code;

FIG. 14a schematically illustrates an imaging system in one embodimentof the invention, when applied, by simulation, to a single dot of atwo-dimensional matrix barcode;

FIG. 14b schematically illustrates an imaging system in one embodimentof the invention, when applied, by simulation, to a two-dimensionalmatrix barcode;

FIG. 15 shows exemplary zero- and first-order images of two-dimensionalmatrix barcodes printed on labels, imaged by an imaging system in oneembodiment of the invention;

FIG. 16 shows the exemplary result (upper-right chart) of thecolumn-by-column deconvolution (or similar non-linear process) from atwo-dimensional matrix barcode image containing zero- and first-ordercomponents (left-hand image), as well as the comparison of the averageof all spectrum curves of the upper-right chart to reference spectralinformation (lower-right chart);

FIGS. 17 to 19 schematically illustrate three imaging systems in threeembodiments of the invention, respectively;

FIGS. 20 and 21 schematically illustrate the generation of a measure ofauthenticity of an object, in two embodiments of the invention, whereinthe image sensor arrangement images the non-dispersed part and dispersedpart in a plurality of illumination periods;

FIGS. 22, 23 a and 23 b are flowcharts of imaging methods in threeembodiments of the invention, wherein the generation of the measure ofauthenticity of an object follows the image sensor arrangement imagingthe non-dispersed part and dispersed part in a plurality of illuminationperiods;

FIGS. 24a and 24b show images of a soft-drink can cap without (FIG. 24a) and with a mask (FIG. 24b ) acquired using an imaging system in oneembodiment of the invention;

FIG. 25 shows examples of images of a soft-drink can cap acquiredwithout a physical mask but excited in two different illuminationperiods by blue light (left-hand image) and green light (right-handimage), in one embodiment of the invention;

FIG. 26 shows examples of background-subtracted images using twodifferent linear combinations, in one embodiment of the invention;

FIG. 27 shows examples of extracted spectra with and without applyingthe DIBS algorithm on acquired images, in one embodiment of theinvention;

FIG. 28 shows the spectral reflectivity of two different colourpigments;

FIG. 29 shows the typical relative spectral distribution of a white LED;

FIG. 30 shows the typical relative spectral distribution of anincandescence bulb at 3000 K temperature, compared to the one of thesun;

FIG. 31 shows the excitation spectrum and emission spectrum of anexemplary fluorescent dye;

FIGS. 32 and 33 show the emission and excitation spectra for exemplaryphosphorescent phosphor pigments;

FIG. 34 is a schematic diagram of an exemplary implementation of acomputing unit according to one embodiment of the invention;

FIGS. 35a to 35d schematically illustrate examples of imaging period(s)and illumination period, in four embodiments of the invention; and

FIG. 36 schematically illustrates an imaging system comprising, on theone hand, an imaging device comprising an image sensor arrangement,wherein the imaging device is a mobile phone having a camera, and, onthe other hand, an imaging accessory comprising a dispersive imagingarrangement.

DETAILED DESCRIPTION

The present invention shall now be described in conjunction withspecific embodiments. These specific embodiments serve to provide theskilled person with a better understanding, but are not intended torestrict the scope of the invention, which is defined by the appendedclaims. A list of abbreviations and their meaning is provided at the endof the detailed description.

FIG. 1 schematically illustrates an imaging system 200 in one embodimentof the invention. System 200 aims at imaging an object 10 and generatinga measure of authenticity of object 10, i.e. an article. Object 10 mayfor example be, without being limited to, a bottle or can of beer, wine,liquor or soft-drink, a pack, packet or box of cigarettes or cigars, amedicine pack, a bottle of perfume, or any other excisable goods, abanknote, a value paper, an identity document, a card, ticket, label,banderol, security foil, security thread or the like. Object 10 has atleast one part, surface or side bearing a visible or invisible mark,logo, sign, image, or pattern, for example printed with a printing inkand/or coating, either printed on a label apposed on object 10 orprinted directly on object 10 (such as on a cap, capsule or the like ofobject 10, wherein the cap or capsule may for example have a colouredbackground). The expected spectral response of said part, surface orside, and possibly the ink thereon (which may or may not have, forexample, photoluminescent properties), when subject to particularillumination conditions, is known and constitutes the reference spectralinformation.

System 200 comprises an arrangement 60, hereinafter referred to as“image sensor arrangement” 60, consisting in one or more image sensors.System 200 also comprises another arrangement 30, hereinafter referredto as “dispersive imaging arrangement” 30, consisting in one or moreoptical elements.

In one embodiment, image sensor arrangement 60 comprises one or morearray CCD or CMOS detectors to record the intensity distribution of theincident electromagnetic energy. Dispersive imaging arrangement 30 notonly disperses electromagnetic energy but may also gatherelectromagnetic energy from object 10 and focus the electromagneticenergy rays to produce an image of object 10 onto an image plane whereimage sensor arrangement 60 is positioned. In one embodiment, dispersiveimaging arrangement 30 comprises, on the one hand, at least one of adiffractive element, a refractive element, one or more lenses, and anobjective, in order to produce an image of object 10 onto the imageplane where image sensor arrangement 60 is positioned, and, on the otherhand, a long pass filter (also called “long-wavelength pass filter”) inorder to limit the spectral range used for authentication.

System 200 may also comprise optionally various auxiliary elements (notshown in FIG. 1) such as for example any one or any combination of: a) ahousing for containing, covering and/or protecting dispersive imagingarrangement 30 and image sensor arrangement 60; b) supporting elementsintegrally formed within the housing, or attached thereto, to maintaindispersive imaging arrangement 30 in a fixed or substantially fixedrelative position with respect to image sensor arrangement 60; c) aprotective cover or protective covering means to be used between object10 and dispersive imaging arrangement 30 to avoid parasitic illuminationfrom ambient light and/or sunlight (in this case, a controlledillumination source may be contained within this protective cover); d)additional optical filters (long-pass, bandpass, etc.), which may forexample be advantageous if imaging system 200 operates in luminescencemode, to cut out the irradiation source reflection; e) a controller orcontrolling means or units for controlling the operation of image sensorarrangement 60 and other elements; f) outputting and inputting means forproviding information to and receiving information from an operator,such as a display screen, a keyboard, push-buttons, control knobs, LEDindicator lights, etc. (in that respect, see also FIG. 34 and thecorresponding description); and g) a battery for powering variouselectronic parts of system 200.

Dispersive imaging arrangement 30 is constituted and positioned so that,when electromagnetic radiation 20 from object 10 illuminates arrangement30 or in particular a specific part, surface, side, aperture, or openingthereof, at least part of radiation 20 splits out in differentdirections into at least a non-dispersed part 40 and a dispersed part50. The word “dispersive” means here: that separates in its constituentwavelength components. Arrangement 30 may for example comprise: adiffractive element, a transmission diffraction grating (also knownsimply as “transmission grating”, or rarely as “transmissive diffractiongrating”), a blazed transmission diffraction grating, a volumeholographic grating, a grism (also called “grating prism”), a reflectivediffraction grating, an arrangement comprising a beam splitter and adiffraction grating, an arrangement comprising a beam splitter and adispersive prism, or a combination of any of those. If arrangement 30diffracts radiation 20, non-dispersed part 40 may be referred to as thezero diffraction order part of the radiation, and dispersed part 50 maybe referred to as a non-zero diffraction order part, such as for examplethe negative or positive first diffraction order part of the radiation.

Here are some examples of transmission gratings that may be used in someembodiments of the invention:

-   -   Example 1: Especially for a transmission grating mounted in        front of an objective (see also in that respect FIGS. 4 and 17),        a Thorlabs # GT13-06V (from Thorlabs, Inc., based in Newton,        N.J., U.S.) with grooves density 600 lines per mm (I/mm), blaze        angle 28.7°, size 12.7×12.7×3 mm from Schott B270 glass, may be        used.    -   Example 2: Especially for a transmission grating mounted between        an objective and the image sensor(s) (see also in that respect        FIGS. 5, 6, 18 and 19), a Richardson grating 340056TB07-775R        (from Newport Corporation, based in Rochester, N.Y., U.S.) with        grooves density of 360 I/mm, blaze angle 21°, and size        12.7×12.7×3 mm, may be used.    -   Example 3: Especially for a back-mounted grating for extended        field of view, a Thorlabs # GTU13-06 with grooves density 600        I/mm, blaze angle 22°, and size 12.7×12.7×2 mm from fused        silica, may be used.

Electromagnetic radiation 20 coming from object 10 and illuminatingdispersive imaging arrangement 30 may originate in part or in full fromthe reflection of electromagnetic radiation emitted by anelectromagnetic radiation source (not shown in FIG. 1). Radiation 20from object 10 and illuminating arrangement 30 may alternatively, oradditionally, originate in part or in full from some form ofphotoluminescence (i.e., fluorescence or phosphorescence) of a substanceof object 10 upon or after the illumination of object 10 byelectromagnetic radiation emitted by an electromagnetic radiationsource. In both cases (i.e., radiation by reflection or by some form ofphotoluminescence), the electromagnetic radiation source may, in oneembodiment, be integrated with, or attached to, a housing containingimaging system 200 (or part thereof). Said electromagnetic radiationsource may for example be a light source, an infrared radiating source,and/or an UV radiating source. In one embodiment, the electromagneticradiation source is an illumination source controlled by, or togetherwith, system 200.

Electromagnetic radiation 20 coming from object 10 usually containsradiation of more than one wavelength, especially when object 10 isauthentic. That is, radiation 20 is usually polychromatic in the broadsense of the term, i.e. not necessarily limited to visible colours.Radiation 20 may for example be in any wavelength range encompassedbetween 180 nm (UV radiation) and 2500 nm (infrared radiation), i.e. inthe visible light range and/or outside that range (for example in thenear-infrared (NIR) or short-wavelength infrared (SWIR) range). Theportion of radiation 20 reaching dispersive imaging arrangement 30 thatis actually dispersed may depend on the characteristics of the opticalelement(s) forming arrangement 30. For example, long pass filter may beused to select the spectral range to be analysed.

Furthermore, dispersive imaging arrangement 30 is positioned relative toimage sensor arrangement 60 in such a manner as to allow arrangement 60to simultaneously image in one imaging period (as illustrated by FIG.35a ), to sequentially image in two imaging periods (as illustrated byFIG. 35b ), or to partially simultaneously image in two imaging periods(as illustrated by FIGS. 35c and 35d ), non-dispersed part 40 in a firstportion of arrangement 60 and dispersed part 50 in a second portion ofarrangement 60.

An example of image sensor that may be used in some embodiments of theinvention is: a 1/3-Inch Wide-VGA CMOS Digital Image Sensor MT9V022 fromON Semiconductor, based in Phoenix, Ariz., U.S. That sensor has752-by-480 pixels with size 6 μm forming active imager size withdimensions of 4.51 mm×2.88 mm and diagonal of 5.35 mm.

An imaging period is here defined as being: a) if non-dispersed part 40and dispersed part 50 are simultaneously acquired by image sensorarrangement 60, the period during which both non-dispersed part 40 anddispersed part 50 are acquired (as illustrated by FIG. 35a ), or b) ifnon-dispersed part 40 and dispersed part 50 are sequentially (asillustrated by FIG. 35b ) or partially simultaneously (as illustrated byFIGS. 35c and 35d ) acquired by image sensor arrangement 60, each of theperiod during which non-dispersed part 40 is acquired and the periodduring which dispersed part 50 is acquired.

In one embodiment, each or at least one imaging period has a durationhaving a value selected from the range of 5 to 1200 ms, and preferablyselected from the range of 10 to 800 ms, such as for example 10, 20, 30,50, 75, 100, 150, 200, or 300 ms.

In one embodiment, the duration of the imaging period for imagingnon-dispersed part 40 and the duration of the imaging period for imagingdispersed part 50 differ from each other. This embodiment may beadvantageous in particular when using diffraction gratings havingdifferent efficiencies for the zero- and first-order. For example, theduration of the imaging period for imaging non-dispersed part 40 may be10 ms, whereas the duration of the imaging period for imaging dispersedpart 50 may be 100 ms.

An illumination period (as illustrated by FIGS. 35a to 35d ) is heredefined as being a period during which illumination conditions areconsidered sufficiently constant for the purpose of imagingnon-dispersed part 40 and dispersed part 50, and generating a measure ofauthenticity based thereof.

In one embodiment, the first and second portions of image sensorarrangement 60 are on two different image sensors of arrangement 60.When using two image sensors for imaging non-dispersed and dispersedparts 40, 50 their relative positioning has to be taken into account.

In another embodiment, the first and second portions of arrangement 60are two different portions of a single image sensor. In other words, inthis embodiment, non-dispersed and dispersed parts 40, 50 may becaptured in a single frame.

The configuration (geometry, parameters, etc.) of the optical elementsof dispersive imaging arrangement 60 allows the separation of dispersedpart 50 from non-dispersed part 40 from within the single frame. Shorterwavelengths are less deflected than longer wavelengths. In oneembodiment, system 200 is configured to avoid overlapping of first-orderimage at the shortest wavelength with the zero-order image (see alsoFIG. 8, which schematically illustrates the order separation). A longpass filter may, for example, be used to cut shorter wavelengths as forexample shown in FIG. 8, in order to prevent overlapping of orders.

The portion of electromagnetic radiation 20 illuminating and passingthrough dispersive imaging arrangement 30 (therefore being dispersed inone set of directions and being non-dispersed in another set ofdirections) that is then actually detected by image sensor arrangement60 depends on the characteristics of its image sensor(s). Theelectromagnetic radiation detected by the image sensor(s) may forexample be in any wavelength range encompassed between 180 nm (UVradiation) and 2500 nm (infrared radiation), i.e. in the visible lightrange and/or outside that range (for example in the near-infrared (NIR)or short-wavelength infrared (SWIR) range). In that example, the lowerlimit of 180 nm may be imposed by material constraints of bothdispersive imaging arrangement 30 and image sensor(s) 60, whereas theupper limit of 2500 nm may for example be imposed by the spectralresponse of indium gallium arsenide-based (GalnAs) infrared detectors.In one embodiment, the electromagnetic radiation detected by imagesensor(s) 60 is in the range of visible light. In one embodiment, theelectromagnetic radiation detected by image sensor(s) 60 is in thewavelength range of 180 nm to 2500 nm, more preferably in the range of400 nm to 1000 nm.

Yet furthermore, imaging system 200 is configured for, after imagesensor arrangement 60 has imaged non-dispersed part 40 and dispersedpart 50 in at least one imaging period, generating a measure ofauthenticity of object 10 depending at least on a relation between theimaged dispersed part, the imaged non-dispersed part, and referencespectral information. System 200 thus enables the verification ofwhether, and/or the extent to which, the relation between the imageddispersed part, the imaged non-dispersed part, and the referencespectral information, which represents the expected spectral compositionof electromagnetic radiation 20 coming from object 10, is in accordancewith the expected underlying physics of the system. If so, object 10 islikely to be authentic. Otherwise, it is more likely to be acounterfeit. System 200 thus enables a form of material-basedauthentication, such as for example at least one of: a) material-basedauthentication of the ink used to create a mark printed on object 10,and b) material-based authentication of object 10 itself especially ifobject 10 is luminescing with a specific emission spectrum or has aspecific reflection or absorption spectrum.

The nature of the relation that is looked at, i.e. the relation betweenthe imaged dispersed part, the imaged non-dispersed part, and thereference spectral information, may be understood in the sense that, ifthe reference spectral information corresponds or substantiallycorresponds to the spectral composition of electromagnetic radiation 20coming from imaged object 10, the imaged dispersed part typicallyresembles (non-linear effects may also need to be taken into account)the result of the convolution of the imaged non-dispersed part with thereference spectral information, in which case object 10 is likely to beauthentic. In contrast, if the reference spectral information does notcorrespond to the spectral composition of radiation 20 coming fromimaged object 10, the imaged dispersed part typically noticeably differsfrom the result of the convolution of the imaged non-dispersed part withthe reference spectral information, in which case object 10 is likely tobe a counterfeit.

More generally, the nature of the relation that is looked at, i.e. therelation between the imaged dispersed part, the imaged non-dispersedpart, and the reference spectral information, may also significantlydiffer from a mere convolution, considering the existence of non-lineareffects. The nature of the relation may be determined a) based on theunderlying physics and geometry, b) empirically, and/or c) by simulation(for example, using raytracing methods of commercially availablesolutions, such as e.g. Zemax optical design program, available fromZemax, LLC, based in Redmond, Wash., U.S.).

The underlying physics and geometry may include (i) the properties ofdispersive imaging arrangement 30, image sensor arrangement 60, thetransmission medium in between, etc., and (ii) effects of stretch of theimage (zero- or first-order) in the direction of the dispersion (yaxis), which may be compensated for by mapping of the y axis of theimage (zero- or first-order) to a new y′ axis using a non-linearfunction. The image may be stretched due to 1) non-linear dispersion ofthe grating, 2) projection distortions (with different paths fromarrangement 30 to arrangement 60), and/or 3) optics-specific fieldaberrations (as lenses may distort slightly differently the zero- andfirst-order).

The non-linear effects may also, in one embodiment, be modelled as arelation between the dispersed and non-dispersed images and thereference spectrum in a form being as close to lineartranslation-invariant (LTI) as possible. In such a case, thedetermination of the non-linear effects may be performed for example bya) acquiring several zero- and first-order images of objects 10 with aknown reference spectrum, and b) fitting the non-linear parameters totransform the relation to LTI.

One way to determine the non-linear effects, and therefore the nature ofthe relation to be looked at, may be a mathematical analysis of theoptical system and determination of the correction that has to or shouldbe applied to make the system LTI. This may be done using opticalequations found for example in textbooks such as Yakov G. Sosking,“Field Guide to Diffractive Optics”, SPIE, 2011. This may also be donenumerically using optical software such as for example ZemaxOpticStudio™, available from Zemax, LLC.

In one embodiment, dispersive imaging arrangement 30 diffractselectromagnetic radiation 20 using a diffraction grating, and the imagednon-dispersed part is the image in zero diffraction order of thediffraction grating, whereas the imaged dispersed part is the image in afirst diffraction order of the diffraction grating. An average spectralirradiance of a region of the image may be reconstructed using theimaged non-dispersed and dispersed parts, and then the average spectralirradiance may be compared to the expected spectral irradiance (thereference spectral information). In one embodiment, the grooves profilesof the diffraction grating (e.g. blaze angle) are optimized to spreadmost of the input electromagnetic radiation into these two orders.

In one embodiment, generating a measure of authenticity of object 10comprises authenticating it, i.e. determining that it is likely to beauthentic or not. In one embodiment, generating a measure ofauthenticity of object 10 comprises generating an authenticity measure(or index) such as for example a real value between 0 and 1, wherein ‘0’may mean “fully sure that the object is not authentic” and ‘1’ may mean“fully sure that the object is authentic”.

In practice, the authentication index typically does not reach the value‘1’ for all authentic objects (and ‘0’ for all non-authentic ones).Hence, in one embodiment, a threshold between ‘0’ and ‘1’ is defined(for example a value comprised between 0.80 and 0.90, and in particular0.85) above which the object is considered as authentic, and below whichthe object is considered as non-authentic. This threshold may forexample be defined through measurements on a set of authentic andnon-authentic objects. These measurements typically produce a bi-modaldistribution of indexes (i.e., one part for the authentic objectsconcentrated towards the value ‘1’ and one part for the non-authenticones below, both separated by a gap). The robustness of the method isdirectly related to the extent to which the two parts (modes) of theindex distribution are distant from one another. The threshold may thenbe placed in between either close to the index distribution of theauthentic objects to minimize false positives or closer to thenon-authentic index distribution to minimize false negatives.

If object 10 is, for example, a container or package containing somegoods, the generated measure of authenticity may merely amount to ameasure of authenticity of the goods determined through a mark or signexisting on the container or package (assuming that the container orpackage has not been tampered with), not necessarily directly enablingto authenticate the goods as such.

Since non-dispersed and dispersed parts 40, 50 of the electromagneticradiation may be imaged in one imaging period, and since the imagingenables the determination of the spectral composition of incidentelectromagnetic radiation 20, imaging system 200 may be regarded as aform of snapshot spectral imager in the sense that the scene is notscanned during the imaging process. However, system 200 does not enableor at least does not necessarily enable obtaining the spectralcomposition, i.e. irradiance, of each point (x, y) of the scene, whichis as such not necessary for authentication provided that there is adominant spectral response in the image.

FIG. 2 schematically illustrates an object 10 to be imaged and a system220 in one embodiment of the invention. System 220 comprises both animaging system 200 (as described above with reference to FIG. 1) and anillumination arrangement 210. In one embodiment, system 220 forms asingle device, such as for example a handheld, code reading andauthentication device.

Illumination arrangement 210 generates electromagnetic radiation 21 forilluminating object 10. In one embodiment, radiation 21 has knownparameters (e.g., spectrum, power, homogeneity, etc.) to allowexcitation of e.g. luminescence emission spectra to allow imaging ofobject 10 and/or mark thereon and analysing the emission spectra forauthentication. As explained above with reference to FIG. 1,electromagnetic radiation 20 originates from object 10, and/or markthereon, and reaches imaging system 200.

In one embodiment, system 220 is connected to driving electronics andsensor reading electronics, so that, for example, image data outputtedby imaging system 200 may be transferred to a processing unit for datatreatment.

FIG. 3 schematically illustrates an object 10 to be imaged and a system220 in one embodiment of the invention, as a possible implementation ofthe system illustrated on FIG. 2. System 220 notably comprisesillumination elements 22 arranged around dispersive imaging arrangement30. Although two illumination elements 22 are shown in FIG. 3, anynumber of illumination elements 22 may be provided, such as for examplethree, four or more. Furthermore, in one embodiment, illuminationelements 22 are arranged symmetrically around dispersive imagingarrangement 30. The symmetric arrangement of illumination elements 22around arrangement 30 is advantageous for homogeneous illumination ofthe target surface of object 10.

FIGS. 4 to 6 schematically illustrate three imaging systems 200 in threeembodiments of the invention, respectively, showing possible componentsof dispersive imaging arrangement 30, such as a transmission grating 31,an imaging lens 32, an optical long-pass filter 33, and an additionallens arrangement 34.

Arrangement 30 of FIG. 4 comprises an imaging lens 32, a transmissiongrating 31 mounted in front of lens 32, and an optical long-pass filter33 mounted behind lens 32. This enables to produce low opticalaberrations for both dispersed and non-dispersed images by using thebroad field-of-view of the lens objective.

In arrangement 30 of FIG. 5, both transmission grating 31 and opticallong-pass filter 33 are mounted behind lens 32. This enables to cancelthe dependence of the extracted spectra on the object position along theoptical axis.

In the embodiment of FIG. 6, optical long-pass filter 33 is mounted infront of lens 32, and transmission grating 31 is mounted behind lens 32.Furthermore, an additional lens arrangement 34 is also mounted behindlens 32. This configuration enables to efficiently separate thedispersed and non-dispersed images and avoid dependence on the objectposition along the optical axis.

FIGS. 7 and 8 schematically represent, using a thin lens-gratingapproximation, two imaging systems 200 and marks 11 in two embodimentsof the invention, respectively, to help understand the order separationand definition of minimum wavelength of a spectral range which isanalysed to authenticate mark 11.

In FIG. 7, dispersive imaging arrangement 30 includes a lens, atransmission grating and a long-wavelength pass filter, to createnon-dispersed image 41 (zero-order) and the dispersed image on the imageplane 65 where the image sensor(s) are positioned. Dispersed beams 50-1are for the shortest wavelength λ₁ and create dispersed image 51corresponding to wavelength λ₁.

Imaging system 200 receives electromagnetic energy 20 originating fromobject 10 to create a non-dispersed image 41 of object 10 onto imageplane 65. Non-dispersed part 40 is produced by arrangement 30 in thesame or similar way as an ordinary, non-dispersive imaging arrangementconsisting merely of a lens.

The dispersed part is shifted compared to the non-dispersed part and isblurred by the spectrum of electromagnetic energy 20 impingingarrangement 30. The minimum shift depends on the minimum wavelengthpresent in the spectrum emitted by object 10 or depends on the minimumwavelength transmitted through arrangement 30. The minimum shift mayalso depend on some grating and system parameters (e.g. grooves density,order, and incident angle) which parameters define the angulardispersion of the grating.

The three discrete dispersed images of mark on FIG. 7 correspond todiscrete wavelengths λ₁, λ₂ and λ₃. These discrete wavelengths cantherefore be conveniently resolved since the corresponding images do notoverlap. Furthermore, system 200 separates dispersed image 51 forwavelength λ₁ from non-dispersed image 41, so that, on the one hand, animage of mark may conveniently be read (e.g., to decode the coderepresented by the mark) and, on the other hand, the emission spectra ofthe ink used to print mark may be extracted.

FIG. 8 shows the imaging of an area 12 of object 10, wherein area 12contains a printed mark 11, which may be in any position or orientation.If mark 10 is outside area 12, imaging system 200 should be repositionedso as to have mark 11 within area 12. Non-dispersed image 41 of area 12contains the image of mark 11. Dispersed image 51 of area 12 containsthe image of mark 11.

Image 51 corresponds to the minimum wavelength λ_(min) that can betransmitted by the system and defined by a cut-on wavelength of a longpass filter of arrangement 30. Reference 62 shows the order separation,which, in one embodiment, corresponds to the size of image 41 of area12. In one embodiment, arrangement 30 enables this order separation forthe minimum wavelength λ_(min) so as to efficiently authenticate object10.

In one embodiment, illumination arrangement 210 (not illustrated on FIG.8) illuminates only the portion of object 10 corresponding to area 12.Illumination arrangement 210, together with an optional protective cover(as mentioned above), may be designed to prevent ambient light fromreaching area 12, thus providing better conditions for code reading andauthentication.

FIG. 9a schematically illustrates an imaging system 200 in oneembodiment of the invention, which differs from imaging system 200 ofFIG. 1 in that system 200 of FIG. 9a specifically consists in a singleimaging device. In addition to dispersive imaging arrangement 30 andimage sensor arrangement 60 described with reference to FIG. 1, system200 comprises a processing unit 70 configured for receiving datarepresenting the imaged non-dispersed and dispersed parts (as detectedby arrangement 60), generating the measure of authenticity as describedwith reference to FIG. 1, and outputting information 80 representing thegenerated measure of authenticity to any kind of user interface of theimaging device and/or to an output port for transmission to one or moreother external devices (not shown in FIG. 9a ).

In one embodiment, the imaging device making up imaging system 200 ofFIG. 9a is a hand-held device. Such an imaging device can therefore beregarded as a hand-held audit device capable of generating a measure ofauthenticity of an object, and providing the authenticity measure to,for example, the device's operator.

FIG. 9b schematically illustrates a system 220 in one embodiment of theinvention, wherein system 220 comprises both an imaging system 200 andan illumination arrangement 210, and wherein system 220 is an imagingdevice. In other words, the embodiment of FIG. 9b may be regarded acombination of the embodiments described with reference to FIGS. 9a and2. In one embodiment, the imaging device making up system 200 of FIG. 9bis a hand-held device.

FIG. 10a schematically illustrates an imaging system 200 in oneembodiment of the invention, which differs from imaging system 200 ofFIG. 1 in that system 200 of FIG. 10a is shown as specificallycomprising more than one device. Namely, in the example of FIG. 10a ,system 200 comprises two devices: on the one hand, an imaging device 100comprising dispersive imaging arrangement 30 and image sensorarrangement 60 described with reference to FIG. 1, and, on the otherhand, a processing device 110 comprising a processing unit 70.Processing device 110, rather than imaging device 100, generates themeasure of authenticity (as described with reference to FIG. 1). To doso, data 90 representing the imaged non-dispersed and dispersed parts istransmitted from imaging device 100 to processing device 110. Data 90may be transmitted on any suitable wired or wireless channel using anytransmission format (such as for example using Internet Protocol (IP)packets, optionally encrypted). Then, within processing device 110, themeasure of authenticity is generated by processing unit 70, andinformation 80 representing the generated measure of authenticity maythen be outputted to a user interface of processing device 110 and/or toan output port for transmission to one or more other external devices(not shown in FIG. 10a ).

FIG. 10b schematically illustrates a system 220 in one embodiment of theinvention, wherein system 220 comprises an imaging device 100 and saidimaging device 100 comprises an image sensor arrangement 30, adispersive imaging arrangement 60 and an illumination arrangement 210,but imaging device 100 is not configured to actually generate themeasure of authenticity. In other words, the embodiment of FIG. 10b maybe regarded a combination of the embodiments described with reference toFIGS. 10a and 2.

In one embodiment, imaging device 100 of any one of FIGS. 10a and 10b isa hand-held device.

In one embodiment, processing unit 70 of any one of FIGS. 9a, 9b, 10aand 10b forms part of a computing unit such as for example the oneillustrated with reference to FIG. 34 (which is discussed below). Insuch a case, processing unit 70 of FIG. 9a or 9 b and processing unit503 of FIG. 34 may actually be the same element. Likewise, in such acase, processing unit 70 of FIG. 10a or 10 b (within processing device110) and processing unit 503 of FIG. 34 may actually be the sameelement.

In some embodiments, the imaging device making up imaging system 200 ofFIG. 9a or 9 b, or imaging device 100 illustrated in FIG. 10a or 10 bcomprises a handle integrally formed with the housing, or attachedthereto, to enable an operator to hold the imaging device towards to theobject to be imaged and authenticated.

In one embodiment, the imaging device making up imaging system 200 ofFIG. 9a or making up system 220 of FIG. 9b , or imaging device 100illustrated in any one of FIGS. 10a and 10b further comprises a storageunit (not shown in any of FIGS. 9a, 9b, 10a, and 10b ) for storing, forexample, the reference spectral information which is known in advanceand used for generating the measure of authenticity. The referencespectral information may be stored in the form of a reference spectralprofile.

FIG. 11 is a flowchart of a method in one embodiment of the invention,which makes use of an image sensor arrangement 60 and a dispersiveimaging arrangement 30, as described above with reference to FIGS. 1 to10 b. The method comprises the steps of imaging s300, by arrangement 60,in at least one imaging period, non-dispersed part 40 and dispersed part50, and generating s400 a measure of authenticity of object 10 dependingat least on a relation between the imaged dispersed part, the imagednon-dispersed part, and reference spectral information. Step s400 iscarried out through convolution or deconvolution operation(s) (asdiscussed below with reference to FIGS. 12a to 12c ) or throughconvolution-like or deconvolution-like operation(s) to take into accountnon-linear effects as explained above.

If imaging step s300 consists in imaging non-dispersed part 40 anddispersed part 50 in a single illumination period, step s300 precedesgenerating step s400, usually without overlap. However, if step s300consists in imaging non-dispersed part 40 and dispersed part 50 in aplurality of illumination periods (typically under differentillumination conditions), imaging step s300 and generating step s400 mayoverlap (not shown in FIG. 11). Namely, the process of generating s400the measure of authenticity may begin based on image data recordedduring one or more illumination periods while imaging step s300 is stillunder way. In one embodiment, generating s400 the measure ofauthenticity depends at least on the extent to which the imageddispersed part corresponds to a convolution of the imaged non-dispersedpart and the reference spectral information. This may be implemented indifferent ways as illustrated by FIGS. 12a to 12 c.

In particular, in a first sub-embodiment, illustrated by the flowchartof FIG. 12a , generating s400 the measure of authenticity comprises:deconvolving s410 the imaged dispersed part by the imaged non-dispersedpart, and determining s420 the extent to which the result corresponds tothe reference spectral information.

In a second sub-embodiment, illustrated by the flowchart of FIG. 12b ,generating s400 the measure of authenticity comprises: deconvolving s430the imaged dispersed part by the reference spectral information, anddetermining s440 the extent to which the result corresponds to theimaged non-dispersed part; and

In a third sub-embodiment, illustrated by the flowchart of FIG. 12c ,generating s400 the measure of authenticity comprises: convolving s450the imaged non-dispersed part and the reference spectral information,and determining s460 the extent to which the result corresponds to theimaged dispersed part.

A possible implementation of step s400 in this third sub-embodiment maybe described as follows:

In step s450, a synthetic first diffraction order image is computed byconvolving the known spectral signature of the authentic ink (i.e., thereference spectral information) with the zero-order image (i.e., theimaged non-dispersed part). Then, in step s460, a cross-correlationbetween the acquired first-order image (i.e., the imaged dispersed part)and the synthetic first-order image (i.e., the result of step s450) isused to compare them and generate a similarity parameter. Thiscorrelation may be performed not only on the images but also the firstand second derivatives of the images to output three similarityparameters. Then, a decision is made by for example applying classifiersbased on machine learning algorithms on the similarity parameter sets toauthenticate mark on object 10.

A convolution might, however, not always lead to the best results due tothe existence of non-linear effects (as discussed above). Thus, in oneembodiment of the invention, rather than carrying out a convolution instep s450, a model or function may be used, which may be determined inadvance using instrument calibration data, measurements, modelling or acombination thereof. The model or function is a computation model forcomputing a synthetic first-order image (i.e., a synthetic dispersedpart) from a given zero-order image (i.e., the imaged non-dispersedpart) and a known spectrum (i.e., the reference spectral information).Similar considerations apply to deconvolving steps s410 and s430, whichmay be replaced by other models or functions.

In order to carry out the comparison part of step s460 in thisimplementation, the acquired first-order image (i.e., the imageddispersed part) and the synthetic first-order image (i.e., the output ofstep s450) are compared, and one or several matching similarity valuesare computed.

In one embodiment, the matching value is the cross-correlation value ofthe two images, i.e. the acquired first-order image and syntheticfirst-order image. In another embodiment, the matching value is thecross-correlation value of the derivative of the two images. In afurther embodiment, the matching value is the cross-correlation value ofthe second derivative of the two images. In yet another embodiment, morethan one matching values are extracted from a combination of thepreviously proposed matching values. The computations may take place onthe entire first-order images, or on a subset of it (region ofinterest). In one embodiment, the first-order image region of interestis the boundary box of the authentication mark. The boundary box is thesmallest convex shape that contains the authentication mark. In anotherembodiment, an additional set of correlation values is computed based onthe so-called DIBS images. The DIBS technique and the meaning of theDIBS images will be apparent from the explanations provided below withreference to FIGS. 24a to 27.

In order to carry out the decision part of step s460 in thisimplementation, a decision algorithm is used to classify a measuredsample into at least two categories: “genuine” or “fake”. Known machinelearning algorithms may be used for that purpose, such as: supportvector machine (SVM), decision trees, K-nearest neighbors algorithm(KNN), etc. In one embodiment, the learning features are theabove-described similarity matching values. In one embodiment, otherlearning features are used, which are not related to cross-correlationssuch as for example the standard deviation of the pixel values (i.e.,intensity values) of the first-order image, or the standard deviation ofthe pixel values of the zero-order image.

In one embodiment, the standard deviation values and several sets ofsimilarity matching values from images obtained under differentexcitation wavelengths (e.g. red, green or blue LED) are used. Forexample, one set of learning features used to describe one sample may beas shown in the following table:

Feature 1 Value of the correlation of the acquired first-order imagewith the synthetic first-order image when illuminated by a blue LEDFeature 2 Value of the correlation of the first derivative of theacquired first-order image with the first derivative of the syntheticfirst-order image when illuminated by a blue LED Feature 3 Value of thecorrelation of the second derivative of the acquired first-order imagewith the second derivative of the synthetic first-order image whenilluminated by a blue LED Feature 4 Value of the correlation of theacquired first-order image with the synthetic first-order image whenilluminated by a green LED Feature 5 Value of the correlation of thefirst derivative of the acquired first-order image with the firstderivative of the synthetic first-order image when illuminated by agreen LED Feature 6 Value of the correlation of the second derivative ofthe acquired first-order image with the second derivative of thesynthetic first-order image when illuminated by a green LED Feature 7Value of the standard deviation of the acquired first-order image whenilluminated by a blue LED Feature 8 Value of the standard deviation ofthe acquired first-order image when illuminated by a green LED

In one embodiment, the classifier may be trained in advance on aheterogeneous dataset consisting of randomized genuine samples andnon-genuine samples.

During the decision phase of the method, the classifier may classify thegiven samples using the features input into the classifier.

The above-referred possible implementation of step s400 in the thirdsub-embodiment has been tested using classification algorithms (in thatrespect see for example: David Barber, “Bayesian Reasoning and MachineLearning”, Cambridge University Press 2011) as described in thefollowing table:

Learning The used features were the correlation value, first featuresderivative correlation value, second derivative correlation value, allthree for blue and green LED excitation, correlation of DIBS values (asdiscussed below), and first- order standard deviation. Training A KNNclassifier was trained on 340 samples (130 genuine and 210 non-genuine).Results When tested against a separate test set of 175 images consistingof never-seen-before backgrounds and codes, the classification accuracywas: 94.29% with 10 false positive and 0 false negative.

Compared with imaging spectrometers used for scientific observations,the approach in the embodiments described with reference to FIGS. 12a to12c is not focused on reconstructing a hypercube that contains spectralinformation for every pixel in the acquired image. The approach aims atcreating one synthetic image with the assumption that there is only onedominant spectrum involved (the genuine mark spectrum). The computationrequired to produce that synthetic image consists mainly in severalone-dimensional convolutions. In comparison with the computation andmemory required to compute a hypercube, the approach is less expensive.Furthermore, the application of a machine learning classifier is alsofast and lightweight.

In one embodiment, the convolution or deconvolution operation(s) of steps400 are performed per line of the image along the diffractiondirection. Furthermore, when deconvolution step s410 of the embodimentdescribed with reference to FIG. 12a is carried out on a line-by-linemanner, the result of the deconvolution may be averaged to reduce noiseand cancel possible modulation by the background non-uniformities, priorto comparing the result against the reference spectral information aspart of step s420.

In one embodiment, as illustrated by the flowchart of FIG. 13,generating s400 the measure of authenticity further comprises decodings492 a code from a marking within the imaged non-dispersed part andverifying s494 the authenticity of the code. This enables the snapshotimaging of the marking for decoding s492 and verification s494 (based onthe imaged non-dispersed part, i.e. based on the “direct” image), andthen using the output of the code verification process in addition tothe verification based on the relation between the imaged dispersedpart, the imaged non-dispersed part, and the reference spectralinformation, to generate the measure of authenticity. For example, inone embodiment, object 10 is regarded as authentic only if bothverifications, i.e. the code-based verification and the spectrum-basedverification, are successful. In other words, spatial information of amarking or printed code as well as spectral emission information of themarking or printed code—which may have been printed for example using aphotoluminescent ink—can both be obtained for the purpose ofauthentication.

In one embodiment, the step of decoding s492 the code is used to obtaininformation based on which the expected spectral composition of theelectromagnetic radiation from object 10 and therefore the referencespectral information to be used for the spectrum-based authenticationverification in step s400 can be retrieved (e.g. through a database). Insuch a manner, several different code families each associated with adifferent ink (and hence a different reference spectrum) can be printedon different classes of products and authenticated with the same device.

In one embodiment, the marking comprises at least one machine readablecode, which may for example comprise at least one of a linear barcodeand a matrix barcode (e.g., a printed Data Matrix code or QR code). Itis therefore possible, in some embodiments of the invention, not only todecode a two-dimensional matrix barcode (or the like) but also to carryout material-based authentication using the spectrum of the radiationcoming from object 10, the radiation spectrum corresponding for exampleto the fluorescence emission spectrum of the ink used for the marking.

In one embodiment, the marking comprises single spectral characteristicsat least over one region of the marking. The marking may also comprisesingle spectral characteristics over the whole marking.

In one embodiment, a mask is intentionally provided, as part of imagingsystem 200 and in addition thereto, on object 10 or in the vicinitythereof to reveal only a portion of object 10. This is advantageous inthe case the whole object carries a substance having the referencespectral information or a large marking which covers the whole image.The mask artificially creates a transition from non-marked to markedarea even if there would be no such transition without the mask. In oneembodiment, imaging system 200 does not use any slit between dispersiveimaging arrangement 30 and object 10. Not using a slit is advantageousin that this enables the simultaneous acquisition of an image and thespectrum thereof, without notably having to scan (by moving the imagingdevice or spectrometer) the surface of the object to measure thespectrum for each position.

Now, before describing further embodiments of the invention, it may beuseful to discuss some of the advantages brought about by someembodiments thereof, especially compared to prior art systems.

The above-described imaging systems and methods in accordance with someembodiments of the invention are advantageous because they allow theconstruction of simple, compact, snapshot-based (non-scanning),low-cost, and versatile devices, which may for example be incorporatedin hand-held audit devices. Acquiring images of both the non-dispersedpart of the electromagnetic radiation and the dispersed part thereofindeed suffices, together with the reference spectral information, whichis known in advance, to generate the measure of authenticity.

In contrast, imaging spectrometers used for scientific observations, asmentioned above, are typically complex, expensive or bulky. This isbecause these prior art systems usually aim at obtaining high-resolutionspatial and spectral information about all regions of the object orscene.

Mechanical scanning of different bandpass filters in front of an imagerallows reconstruction of a spectral irradiance map of the object I(x, y,λ). However, the time to scan all filters and the complexity andfragility of the scanning mechanism makes the optical system cumbersome,not rugged and costly to implement.

Tuning systems based on Fabry-Perot interferometer or multistage liquidcrystals avoid mechanical complexity but require high-quality and costlyoptical components (i.e. interferometric mirrors). The scanning of thefilter parameters needed to acquire full set of images can be slow andcan become another limitation for the use in handheld authenticationsystems.

Snapshot solutions relying on simultaneous imaging of an object througharray of bandpass filters can achieve fast data acquisition and areespecially adapted to handheld audit devices. Furthermore, such systemsare compact and fit easily in a small volume of a hand-held device. Thelimited number of different passband filters is, however, a drawback,and it is also difficult to obtain suitable lenslet arrays. In addition,the spectral bands of the filter array have to be optimized to the inkspectral response, which prevents the use of off-the-shelf filter arrayswhile custom filter arrays are typically expensive to design andmanufacture.

The example of a grating-based imager using computer tomography (i.e.CTIS) requires either a complex holographically recorded Kinoform typegrating or several crossed gratings able to disperse the light in set oforders around the zero order. The need of several gratings complicatesthe setup and furthermore, the exposure time should be extended tocompensate low efficiency in higher diffraction orders. The dataacquisition is therefore slowed, rendering the setup unsuitable for ahand-held device. Such arrangements also require expensive large sensorswith multi mega-pixels and extensive calculation for the tomographyinversion.

The coded aperture imagers are as slow as the CTIS devices. Moreover,there is an intrinsic problem to reconstruct the full spectrum forspecific design of the coded aperture. Meanwhile, integral fieldspectrometers require cumbersome image slicing optics and requirerelatively large surface image sensors.

Imaging Fourier transform spectrometers are complex instruments relyingon expensive interferometers or birefringent prisms. In either case, thespectrometers require scanning of either an air gap or an angularorientation of the elements to obtain spectra that makes them slow andfragile.

The above-described prior art setups require complex optics and datatreatment algorithms to calculate a full spectral data cube I(x, y, λ),which is actually not required for authentication purposes. Theinventors have found none of these prior art setups suitable for aneconomical, compact, robust, and fast auditing device based on aspectral imager.

Let us now describe further embodiments of the invention, which may helpunderstand some aspects and advantages of the invention.

In one embodiment, imaging system 200 has an optical setup with atransmission diffraction grating 31 mounted in front of a lens objective32 in a dispersive imaging arrangement 30 which is then arranged infront of an image sensor arrangement 60, as schematically illustrated onthe left-hand side of both FIGS. 14a and 14b . System 200 uses a lensobjective 32 of model EO57907 from Edmund Optics Ltd (based in York, UK)with f/2.5 and f=3.6 mm focal length. The dispersive element inarrangement 30 is a transmission diffraction grating 31 of type GT13-06Vfrom Thorlabs, Inc., as mentioned above, with 600 lines-per-mm and 28.7°blaze angle. Area 12 of object 10 is within the field of view of imagingsystem 200.

FIG. 14a also shows, on the right-hand side of the drawing, thesimulated dispersion of a single dot (of, for example, a two-dimensionalmatrix barcode) at three discrete wavelengths obtained by means oftransmission diffraction grating 31 installed in front of imagingobjective 32. The dispersion of the diffraction grating 31 obtained froma Zemax OpticStudio™ simulation is shown. One can see the direct (“Order0”) and dispersed images in first positive (“Order 1”) and firstnegative (“Order −1”) orders of the single dot (with diameter of 0.5 mm)onto the image space for three discrete wavelengths.

More complex marks such as full two-dimensional matrix barcodestypically produce smeared images in the first order of the grating 31due to the specific, broader emission spectra of the inks, and anassociated overlap of the successive spread dots in the direction ofdiffraction is observed, as illustrated on the right-hand side of FIG.14b . In particular, FIG. 14b shows the simulated dispersion of a datamatrix with the non-dispersed image (“Order 0”) and two imagesassociated with both dispersed orders, i.e. the first positive order(“Order 1”) and the first negative order (“Order −1”), assuming equalefficiency of grating 31 for all three orders. The direct image in thezero order of the grating is not influenced by the grating (except forintensity attenuation) and can be used to decode a printedtwo-dimensional matrix barcode. The scale shown on FIG. 14b is inintensity in arbitrary units (“I, a.u.”).

Examples of zero- and first-order real images of a two-dimensionalmatrix barcode printed with two inks, i.e. ink type 1 and ink type 2,are shown in FIG. 15. Namely, FIG. 15 shows the zero- and first-orderreal images of two-dimensional matrix barcodes printed on labels, with,on the left-hand side of FIG. 15, ink type 1 excited with blue LED light(peak 450 nm), and, on the right-hand side of FIG. 15, ink type 2excited with red light (peak 640 nm).

It can be observed that the images in zero- and first-order of thegrating can be recorded simultaneously (as illustrated by FIG. 35a ),sequentially (as illustrated by FIG. 35b ), or partially sequentially(as illustrated by FIGS. 35c and 35d ), as they both fit on the arraydetector. Further, the efficiency of the grating for both orders issimilar allowing recording both orders with the same exposure time. Theefficiency in nth order of a grating is the ratio of diffracted power inthe nth order to the total incident power.

The dispersed image in the first order is a convolution (or aconvolution-like function) of the zero-order image of thetwo-dimensional matrix barcode with the ink fluorescence emissionspectrum. As a result, the ink emission spectrum can be extracted bydeconvolution (or deconvolution-like operation) of the first-order imageusing the spatial information from the zero-order image that is notaffected by the grating dispersion.

A deconvolution algorithm based on fast Fourier transform (FFT) may forexample be used to extract the spectrum of the ink. It may use a set ofcolumns from the images, extracted along the grating dispersiondirection, comprising intensity profiles from the zero- and first-orderimages.

FIG. 16 shows an exemplary result (upper-right chart) of thecolumn-by-column deconvolution (or similar non-linear process) from atwo-dimensional matrix barcode image containing zero- and first-ordercomponents (left-hand image), as well as the comparison of the averageof all spectrum curves of the upper-right chart to reference spectralinformation (lower-right chart), i.e. the spectrum of ink type 1. Eachof about 250 columns is subject to deconvolution (or similar non-linearprocess) and produces a spectrum. The spectra obtained from all columnsare then averaged. This averaging reduces the noise (due for example toartefacts that might be created by the deconvolution or similarnon-linear process) and cancels local contribution from the background,which may occur on limited parts of the two-dimensional matrix barcode.Hence, the reconstructed spectral profile is an average for the entireprinted two-dimensional matrix barcode being observed because it isassumed that all matrix dots are printed with the same ink and there isno significant contribution of background to the emission spectrum.

FIGS. 17 to 19 schematically illustrate three imaging systems 200 inthree embodiments of the invention, respectively, showing possiblecomponents of dispersive imaging arrangement 30, such as a transmissiongrating 31, an imaging lens 32, an optical long-pass filter 33, and anadditional lens arrangement 34. Area 12 of item 10 can be imaged byarrangement 30, considering its field of view (FOV) 15. Non-dispersedimage 41 of area 12 and dispersed image 51 of area 12 corresponding toshortest wavelength are both indicated. Reference 61 is the window 61 ofthe image sensor(s) 63.

Arrangement 30 of FIG. 17 comprises an imaging lens 32, a transmissiongrating 31 (600 I/mm) mounted in front of lens 32 (lens objective EdmundOptics 57907), and an optical long-pass filter 33 mounted behind lens32. As already explained with reference to FIG. 4, this enables toproduce low optical aberrations for both dispersed and non-dispersedimages by using the broad field-of-view of the lens objective.

Since grating 31 is mounted in front of imaging lens 32, it deflects thebeams differently for zero- and first-order and imaging lens 32 receivesthe input beams at different angles. In such a configuration, a wide-FOVimaging lens 32 is used which allows incident beams at angles specificfor the first order.

In arrangement 30 of FIG. 18, both transmission grating 31 (360 I/mm)and optical long-pass filter 33 are mounted behind lens 32 (lensobjective Edmund Optics 57907). As already explained with reference toFIG. 5, this enables to cancel the dependence of the extracted spectraon the object position along the optical axis.

In arrangement 30 of FIG. 19, optical long-pass filter 33 is mounted infront of lens 32, and transmission grating 31 (600 I/mm) is mountedbehind lens 32 (lens objective Edmund Optics 57907). Furthermore, anadditional lens arrangement 34 is also mounted behind lens 32. Asalready explained with reference to FIG. 6, this configuration enablesto efficiently separate the dispersed and non-dispersed images (beingapproximately double compared to the embodiment of FIG. 18) and avoiddependence on the object position along the optical axis.

Let us now describe further embodiments of the invention involvingimaging over a plurality of illumination periods, first with referenceto FIGS. 20 and 22 and then with reference to FIGS. 21 and 23 a-b. Thesefurther embodiments may naturally be combined with any of theabove-described embodiments.

FIG. 20 schematically illustrates the generation of a measure ofauthenticity of object 10 in one embodiment of imaging system 200. Inthis embodiment, as a first step, image sensor arrangement 60 images theabove-described non-dispersed part 40 and dispersed part 50 in aplurality of illumination periods t₁, t₂, . . . , t_(n). In oneembodiment, n equals 2. In another embodiment, n equals 3. Object 10 isilluminated differently during each illumination period. Eachillumination period may encompass one or two imaging periods (eitheroverlapping or non-overlapping), as schematically illustrated withreference to FIGS. 35a to 35 d.

Then, the measure of authenticity is generated. The generation of themeasure of authenticity comprises the following steps.

First, for each illumination period t_(i) (1≤i≤n), an intermediatemeasure of authenticity k_(i) is generated depending at least on arelation between dispersed part 50 (A_(i)) imaged at the illuminationperiod t_(i), non-dispersed part 40 (B_(i)) imaged at the illuminationperiod t_(i), and a part of the reference spectral information, saidpart of the reference spectral information being associated with howobject 10 has been illuminated during illumination period t_(i). In oneembodiment, intermediate measure of authenticity k_(i) is generated, foreach illumination period t_(i), by determining, for each illuminationperiod t_(i), the extent to which the dispersed part imaged atillumination period t_(i) corresponds to a convolution of thenon-dispersed part imaged at illumination period t_(i) and said part ofthe reference spectral information associated with how object 10 hasbeen illuminated during illumination period t_(i).

Secondly, the measure of authenticity m is generated based on theplurality of intermediate measures of authenticity k₁, k₂, . . . ,k_(n). This is illustrated on FIG. 20 by the exemplary equation: m=f(k₁,k₂, . . . , k_(n)), wherein f is a function such as for example thearithmetic mean of the intermediate measures of authenticity.

FIG. 22 is a flowchart of an imaging method corresponding to the processillustrated by FIG. 20, wherein the generation s400 of the measure ofauthenticity of object 10 follows image sensor arrangement 60 imagings300 non-dispersed part 40 and dispersed part 50 in a plurality ofillumination periods t₁, t₂, . . . , t_(n). The generation s400 of themeasure of authenticity comprises generating s470, for each illuminationperiod t_(i), an intermediate measure of authenticity k_(i) as describedabove, and then generating s475 the measure of authenticity m based onthe plurality of generated intermediate measures of authenticity k₁, k₂,. . . , k_(n).

In one embodiment, generating s470, for each illumination period t_(i),the intermediate measure of authenticity k_(i) comprises: determining,for each illumination period t_(i), the extent to which the dispersedpart imaged at illumination period t_(i) corresponds to a convolution ofthe non-dispersed part imaged at illumination period t_(i) and said partof the reference spectral information associated with how object 10 hasbeen illuminated during illumination period t_(i).

In one embodiment (not illustrated in FIG. 22), the intermediate measurek_(i) of authenticity of each illumination period is generated s470without waiting for the completion of imaging step s300 for allillumination periods. That is, step s470 can be carried out while steps300 is still under way. For example, as soon as image sensorarrangement 60 has imaged non-dispersed part 40 and dispersed part 50for illumination period t₁, intermediate measure of authenticity k₁ maybe generated s470 for illumination period t₁ and then stored, so thatgenerating step s475 may later be carried out based on all storedintermediate measures of authenticity k₁, . . . , k_(n).

FIG. 21 schematically illustrates the generation of a measure ofauthenticity of object 10, in another embodiment of the invention. Inthis embodiment, as in the embodiment described with reference to FIGS.20 and 22, image sensor arrangement 60 first images non-dispersed part40 and dispersed part 50 in a plurality of illumination periods t₁, t₂,. . . , t_(n). The value n may for example be equal to 2 or 3, andobject 10 is illuminated differently during each illumination period.Again, each illumination period may encompass one or two imaging periods(either overlapping or non-overlapping), as schematically illustratedwith reference to FIGS. 35a to 35d . The measure of authenticity is thengenerated through the following steps:

The imaged non-dispersed part {B₁, B₂, . . . , B_(n)} is processed basedat least on the non-dispersed part B₁ imaged at a first illuminationperiod t₁ among the plurality of illumination periods t₁, t₂, . . . ,t_(n) and the non-dispersed part B₂ imaged at a second illuminationperiod t₂, to produce the processed imaged non-dispersed part B_(x). Allimages B₁, B₂, . . . , B_(n) may also be taken into account to producethe so-called processed imaged non-dispersed part B_(x). That is, theprocessed imaged non-dispersed part B_(x) may be generated based on thenon-dispersed parts imaged at a first to nth illumination periods t₁,t₂, . . . , t_(n). Likewise, the processed imaged dispersed part isgenerated based at least on the dispersed part A_(l) imaged at a firstillumination period t₁ among the plurality of illumination periods t₁,t₂, . . . , t_(n) and the dispersed part A₂ imaged at a secondillumination period t₂, to produce the so-called processed imageddispersed part A_(x). All dispersed parts A₁, λ₂, . . . , λ_(n) imagedat all the illumination periods t₁, t₂, . . . , t_(n) may alternativelybe taken into account to produce the processed imaged dispersed partA_(x).

Then, the measure of authenticity m is generated depending at least on arelation between the processed imaged dispersed part A_(x), theprocessed imaged non-dispersed part B_(x), and reference spectralinformation. In one embodiment, the measure of authenticity m isgenerated based at least on the extent to which the processed imageddispersed part A_(x) corresponds to a convolution of the processedimaged non-dispersed part B_(x) and reference spectral information.

FIGS. 23a and 23b are two flowcharts of imaging methods in twoembodiments corresponding to the process illustrated by FIG. 21, whereinthe generation s400 of the measure of authenticity follows image sensorarrangement 60 imaging s300 non-dispersed part 40 and dispersed part 50in a plurality of illumination periods t₁, t₂, . . . , t_(n).

Namely, referring to FIG. 23a , after imaging s300, by image sensorarrangement 60, non-dispersed part 40 and dispersed part 50, in aplurality of illumination periods t₁, t₂, . . . , t_(n), the measure ofauthenticity is generated s400. Step s400 comprises, first, generatings482 the so-called processed imaged non-dispersed part B_(x) based atleast on the non-dispersed parts B₁, B₂ imaged at a first and secondillumination period t₁, t₂, and preferably based on all non-dispersedparts B₁, B₂, . . . , B_(n) imaged at illumination periods t₁, t₂, . . ., t_(n). Likewise, the so-called processed imaged dispersed part A_(x)is generated s484 based at least on the dispersed parts A₁, A₂ imaged atillumination periods t₁, t₂, and preferably based on all non-dispersedparts A₁, . . . , λ_(n) imaged at illumination periods t₁, . . . ,t_(n). Then, the measure of authenticity m is generated s486 dependingat least on a relation between processed imaged dispersed part A_(x),processed imaged non-dispersed part B_(x), and reference spectralinformation.

In FIG. 23a , steps s482 and s484 are carried out sequentially. However,step s482 may also be carried out after step s484. In one embodiment,steps s482 and s484 are instead carried out in parallel, as illustratedin FIG. 23 b.

In one embodiment, step s482 may be implemented as follows (likewise,step s484 may be implemented in a similar manner): First, a weightingfactor is calculated based on a statistical processing of pixel valuesof the first image data B₁ (i.e., the non-dispersed part imaged atillumination period t₁) and pixel values of the second image data B₂(i.e., the non-dispersed part imaged at illumination period t₂). Then,third image data B_(x) (i.e., the so-called processed imagednon-dispersed part) is generated by calculating a weighted combinationusing the pixel values of said first image data B₁, the pixel values ofsaid second image data B₂, and said weighting factor. Such animplementation may be performed to maximize the image contrast between amarking (e.g. a barcode) and the remaining background, as described inPCT application WO 2014/187474 A1 by the same applicant. WO 2014/187474A1 discloses techniques to enhance the image of a mark or code printedover fluorescing background or other backgrounds. Several images of amark or code are acquired under different illumination conditions, andan image subtraction algorithm suppresses the background to facilitatethe extraction of the printed codes from the images.

This embodiment, which will be described in more detail with referenceto FIGS. 24a to 27, can be regarded as a method to enhance the spectralrecognition and authentication of a mark (such as for example a printedmark) on backgrounds (such as for example complex fluorescingbackgrounds), by using a spectral imager with a dispersive imagingarrangement 30 (such as for example a transmission diffraction grating)and background subtraction using differential images (as described in WO2014/187474 A1). The background subtraction using differential images,as described in WO 2014/187474 A1, will be hereinafter referred to asthe differential illumination background subtraction (DIBS) feature,technique, or algorithm.

This embodiment addresses in particular the following potentialproblems: The imaged non-dispersed part and imaged dispersed partcreated by means of dispersive imaging arrangement 30, as discussedabove, may overlap and, for example, the fluorescing background of a cancap (or the like) could pose problems for decoding and spectrumextraction. One embodiment of the invention to reduce the effect ofoverlap is to use optionally an appropriate mask which hides part of theimage of object 10 to avoid the overlap between the zero- andfirst-order images of the code created by means of arrangement 30. Sucha mask however is physical and may, under certain circumstances, disturbthe code reading by reduction of the useful field of view. Further, themask may complicate the opto-mechanical design of imaging system 200.

The DIBS-based embodiment aims at addressing such problems. It usesimages obtained through arrangement 30 which have an overlap between theorders, and a background subtraction using the DIBS technique isapplied. The DIBS technique reduces the effect of fluorescing background(or the like) on the zero-order images (non-dispersed part 40) andfurther corrects the first-order images (dispersed part 50), thusimproving the spectrum-based generation of the measure of authenticity.This is particularly advantageous when the fluorescing background has anexcitation spectrum which differs from the ink to be authenticated (e.g.matrix code).

An example of images of a sample object 10 with fluorescing backgroundobtained with an imaging system 200 of FIG. 1 is shown in FIG. 24a (animage of soft-drink can cap without using a mask). A region withoverlapping zero- and first-order images of sample object 10 can beobserved in FIG. 24a . In this region, it may be difficult or impossibleto decode a data matrix due to reduced contrast. This causes theextraction of the spectrum (for generating the measure of authenticity)to be difficult or this may lead to significant errors.

Therefore, the image of FIG. 24a has two problems: 1) the backgroundvisible in the zero-order overlaps the first order image, and 2) thebackground emits light which is diffracted in the 1^(st) order andinterfere “spectrally” with the spectral information to beauthenticated. The first problem may be addressed by using a physicalmask. The DIBS technique specifically addresses the second problem, bysignificantly reducing the background signal from the image.

FIG. 24b shows an image of the same sample object 10 taken with aphysical mask in one embodiment of the invention. No overlap between theorders is present which renders an efficient decoding and spectrumextraction possible, but the useful field-of-view may be limited. Such alimitation may, under certain circumstances, restrict the user tooperate the device only with specific orientations possibly leading toan increase of authentication time for a sample object 10.

In accordance with the above-mentioned DIBS-based embodiment, no mask isused, but images are acquired in a plurality of illumination periods t₁,t₂, . . . , t_(n) with several different illuminations and then an imagesubtraction is carried out in accordance with the DIBS technique. Thisreduces the influence of a fluorescing background (or the like) on boththe decoding (if used) and spectrum extraction.

For example, the DIBS algorithm may use two images acquired byilluminating object 10 with blue and green light respectively. As anoutput of the algorithm, an image is obtained which is the difference ofimages taken with blue and green illumination. This image typically hasbetter contrast when it comes to the printed code compared to theinitial images, thus improving the performance of the decoding engine(if used). Furthermore, the resulting image also improves the spectrumextraction using the first-order image (i.e., dispersed part 50) createdby means of dispersive imaging arrangement 30. This effect may beexplained by the different excitation spectra for both the ink used toprint the code and the fluorescing background of object 10 (e.g. asoft-drink can cap). The ink is better excited in blue than in greenwhile the background of the soft-drink can cap has mostly the sameexcitation for both colours. Subtracting the images then leads toincrease of the code contrast and improved spectrum extraction.

FIG. 25 shows examples of images of a soft-drink can cap acquiredwithout a physical mask but excited in two different illuminationperiods by blue light (right-hand image) and green light (left-handimage), in one embodiment of the invention.

FIG. 26 shows examples of background subtracted images using DIBSalgorithm, using respectively the linear combinations B−0.94*G(right-hand image) and 8.22*(B−0.94*G) (left-hand image), in oneembodiment of the invention. In the linear combination B−0.94*G, B is afirst image excited in a first illumination period by blue light, G is asecond image excited in a second illumination period by green light, and0.94 is the weighting factor. In the linear combination 8.22*(B−0.94*G),the significance of B, G and 0.94 are the same as for the first linearcombination, and 8.22 is a scaling factor. Regarding these linearcombinations, the weighting factor and the scaling factor, see equation(1) in WO 2014/187474 A1, page 8, and the corresponding description.

Thanks to the DIBS algorithm, the treated image is more suitable fordecoding and improves the spectrum-based generation of the measure ofauthenticity. FIG. 27 shows examples of extracted spectra with andwithout DIBS algorithm applied on acquired images in one embodiment ofthe invention. The extracted spectra can be compared in FIG. 27, wherethe DIBS pre-treated images allow more precise spectra reconstruction.

Let us now describe further embodiments of the invention applicable toboth the imaging over a single illumination period and the imaging overa plurality of illumination periods. These further embodiments may becombined with any of the above-described embodiments.

In one embodiment, object 10 bears a visible or invisible mark (or sign)printed with a printing ink. Such ink contains coloring and/orluminescing agents, such as dye(s) and/or pigment(s) that are typicallyhard to produce and to reverse-engineer. These optical agents may beclassified into two main classes: 1) optical agents producing specificreflective properties upon controlled illumination, and 2) opticalagents producing luminescence upon controlled illumination.

The expected spectral response of said optical agents, when subject toparticular illumination conditions, is known a priori and constitutesthe reference spectral information.

In the case of reflective properties, the spectral response is calledthe spectral reflectivity, which is the fraction of electromagneticpower reflected per unit of wavelength. For example, FIG. 28 shows thespectral reflectivity of two different color pigments (Microlith® fromBASF AG, based in Ludwigshafen, Germany), as measured with aspectrophotometer in reflectance mode (e.g. model DU-640Spectrophotometer from Beckman Coulter Inc., based in Brea, Calif.,U.S.).

In order for the reflectivity to be determined, a known broadbandillumination source may be used, since the wavelength-dependentreflected electromagnetic radiation 20 (spectral radiance, which ismeasured) depends on the incident spectral composition of theillumination (spectral irradiance). The spectral reflectivity may bedetermined either using a calibrated illumination source (in wavelength)or by comparison with a surface of known spectral reflectivity (such asa reference white surface like Spectralon® from LabSphere, based inNorth Sutton, N.H., U.S.) using a non-calibrated broadband light source.The term “broadband” means that the light source emits at least at allwavelengths in the range of interest. Examples of broadband light sourcespectral distribution are shown for a white LED (e.g., an OSRAM OSLONSSL white LED) in FIG. 29 and tungsten filament lamp (incandescent bulb)in FIG. 30 (Source: Schroeder, D. V., 2003. “Radiant Energy,” onlinechapter for the course, ‘Energy, Entropy, and Everything,’ PhysicsDepartment, Weber State University [accessed May 2016]http://physics.weber.edu/schroeder/eee/chapter6.pdf.).

It can be observed from FIGS. 29 and 30 that the spectrum reflected froma given mark strongly depends on the spectrum of the irradiation source.Therefore, the so-called “reference spectral information” should be thespectral reflectivity (reflectance) of the object or mark. Inembodiments where the reference spectral information is the recordedspectral irradiance, said reference spectral information is thenintrinsically related to the spectral distribution of the irradiationsource, which should preferably be controlled when the referencespectral information is recorded the first time (enrolled) and also whenit is measured to determine the authenticity of object 10.

A second class of optical agents covers luminescent dyes or pigments andhas different requirements in terms of illumination and measurement.

Fluorescent dyes and pigments may be selected for example from perylenes(e.g. Lumogen F Yellow 083, Lumogen F Orange 240, Lumogen F Red 300, allavailable from BASF AG). FIG. 31 (source: WO 2016/042025 A1) shows anexample of excitation and emission spectrum of such a fluorescent dye.In particular, it shows the excitation spectrum 601 and emissionspectrum 602 of a fluorescent dye (Lumogen® F Orange 240 from BASF AG)added in an ink used for printing for example a digital code.Double-headed arrow 603 indicates the wavelength range where theemission spectrum can be used as reference spectral information. It canbe observed from FIG. 31 that the excitation spectrum spans betweenabout 400 and 550 nm and the emission spectrum from about 550 to 700 nm.This requires that the illumination source emits at least in the regionof excitation for the fluorescent dye to be excited, but preferably notin the emission spectral region to avoid interfering with thefluorescence emission to be detected, which is typically several ordersof magnitude weaker than the direct reflection.

This illumination and detection scheme is known in the field ofmeasuring fluorescence and usually comprises a narrow band illuminationsource such as for example a single color LED (a blue one at 450 nm or agreen one at 530 nm may be adapted to excite the Lumogen of FIG. 31) anda long pass optical filter in the detection optical path to cut out anyreflection for the tail of the illumination source in the region ofemission. Optionally, a short pass optical filter may also be arrangedbetween the LED and the object 10 to be authenticated.

FIGS. 32 and 33 show emission and excitation spectra for two exemplaryphosphorescent phosphor pigments: Lumilux® blue SN and Lumilux® greenSN-F2Y from Honeywell International, Inc., based in Morris Plains, N.J.,U.S. The spectroscopic properties shown in FIGS. 32 and 33 were measuredon samples printed with silk-screen inks using a spectrofluorometer(Horiba Jobin Yvon Fluorolog model FLIII-22, from Horiba, based inKyoto, Japan). The approach is the same as for the above-describedfluorescent dyes or pigments. Excitation spectra 501 and 511 andemission spectra 502 and 522 of two phosphorescent pigments are used forprinting marks to be authenticated in the form of patch, logo ordesigns. Black arrow 505 on each of FIGS. 32 and 33 indicates thewavelength peak of a deep blue LED at 410 nm which may be used forexciting the phosphorescent pigments efficiently.

In one embodiment, the reference spectral information is generated priorto operating the system and method of authentication. This may be donethrough a recording and registering of the extracted spectralinformation, in the same or very similar conditions of illumination anddetection (for example using the same device or instrument) as the oneto be used in the field.

In one embodiment, a non-controlled illumination source may be used,provided that its spectral characteristics can be determined, through aspectral measurement and a subsequent correction may be made prior toextracting the measured spectral information from object 10 or mark tobe authenticated.

FIG. 34 is a schematic diagram of an exemplary implementation of acomputing unit 700 that may be used in embodiments of the invention,such as, but not only, for generating the above-discussed measure ofauthenticity.

As illustrated by FIG. 34, a computing unit 700 may include a bus 705, aprocessing unit 703, a main memory 707, a ROM 708, a storage device 709,an input device 702, an output device 704, and a communication interface706. Bus 705 may include a path that permits communication among thecomponents of computing unit 700.

Processing unit 703 may include a processor, a microprocessor, orprocessing logic that may interpret and execute instructions. Mainmemory 707 may include a RAM or another type of dynamic storage devicethat may store information and instructions for execution by processingunit 703. ROM 708 may include a ROM device or another type of staticstorage device that may store static information and instructions foruse by processing unit 703. Storage device 709 may include a magneticand/or optical recording medium and its corresponding drive.

Input device 702 may include a mechanism that permits an operator toinput information to processing unit 703, such as a keypad, a keyboard,a mouse, a pen, voice recognition and/or biometric mechanisms, etc.Output device 704 may include a mechanism that outputs information tothe operator, including a display, a printer, a speaker, etc.Communication interface 706 may include any transceiver-like mechanismthat enables computing unit 700 to communicate with other devices and/orsystems (such as with a base station, a WLAN access point, etc.). Forexample, communication interface 706 may include mechanisms forcommunicating with another device or system via a network.

Computing unit 700 may perform certain operations or processes describedherein. These operations may be performed in response to processing unit703 executing software instructions contained in a computer-readablemedium, such as main memory 707, ROM 708, and/or storage device 709. Acomputer-readable medium may be defined as a physical or a logicalmemory device. For example, a logical memory device may include memoryspace within a single physical memory device or distributed acrossmultiple physical memory devices. Each of main memory 707, ROM 708 andstorage device 709 may include computer-readable media. The magneticand/or optical recording media (e.g., readable CDs or DVDs) of storagedevice 709 may also include computer-readable media. The softwareinstructions may be read into main memory 707 from anothercomputer-readable medium, such as storage device 709, or from anotherdevice via communication interface 706.

The software instructions contained in main memory 709 may causeprocessing unit 703 to perform operations or processes described herein,such as for example generating the measure of authenticity.Alternatively, hardwired circuitry may be used in place of or incombination with software instructions to implement processes and/oroperations described herein. Thus, implementations described herein arenot limited to any specific combination of hardware and software.

FIGS. 35a to 35d schematically illustrate examples of imaging period(s)and illumination period, in four embodiments of the invention. Thesedrawings have been already referred to and elaborated upon throughoutthe above description.

In one embodiment, imaging system 200 comprises, on the one hand, animaging device comprising image sensor arrangement 60 and, on the otherhand, a piece of equipment, hereinafter referred to as “imagingaccessory”, comprising dispersive imaging arrangement 30.

In this embodiment, the imaging device has a built-in camera (includingassociated lenses) and may be a hand-held device, such as for example atleast one of: a mobile phone, a smartphone, a feature phone, a tabletcomputer, a phablet, a portable media player, a netbook, a gamingdevice, a personal digital assistant, and a portable computer device.The imaging device's built-in camera image sensors act as image sensorarrangement 60 in system 200.

As mentioned above, the imaging accessory comprises dispersive imagingarrangement 30, such as for example a transmission diffraction grating,or any other dispersive element as already discussed above withreference to FIG. 1.

The imaging accessory is attachable, directly or indirectly (for examplevia a connecting piece of equipment), to the imaging device so that theimaging accessory's dispersive imaging arrangement 30 is positionedrelative to the imaging device's image sensor arrangement 60 in such amanner that the imaging device and the imaging accessory form an imagingsystem 200 as described above, operable for imaging an object andgenerating a measure of authenticity of the object. In other words, theimaging accessory may be used for example to transform a smartphone intoa portable imaging and authentication system as described above. Theimaging accessory may for example be fixedly positionable over thesmartphone rear camera. The processing and communications capabilitiesof the smartphone may then be used for implementing a processing unit 70of imaging system 200.

Furthermore, if the imaging device has a light source (such as forexample flash LEDs used in a smartphone), said light source may operateas illumination arrangement 210 to illuminate the object 10 to be imagedand authenticated. A smartphone's light source is typically well adaptedfor reflectivity measurements. Alternatively, illumination arrangement210 may be provided as part of the imaging accessory.

This embodiment is advantageous in that the imaging accessory may be apassive accessory, requiring no additional power, and thus providing anaffordable authentication solution.

FIG. 36 schematically illustrates an imaging system 200 in accordancewith the above-described embodiment comprising, on the one hand, animaging device comprising image sensor arrangement 60, wherein theimaging device is a mobile phone having a camera, and, on the otherhand, an imaging accessory 36 comprising dispersive imaging arrangement30. In this exemplary optical setup, imaging accessory 36 comprises adiffraction grating 31 and long pass filter 33 arranged in front of themobile phone camera 64. The mobile phone camera 64 comprises an imagesensor 60 and a built-in lens 66. Optionally, an additional collimatinglens 35 may be positioned in front of imaging accessory 36.

The invention further relates to the following embodiments:

-   Embodiment (X2). Imaging system (200) of claim 1, wherein the    imaging system (200) is an imaging device.-   Embodiment (X3). Imaging system (200) of claim 1, comprising    -   an imaging device (100) comprising the image sensor arrangement        (60) and the dispersive imaging arrangement (30), wherein the        imaging device (100) is not configured to generate the measure        of authenticity.-   Embodiment (X4). Imaging system (200) of embodiment (X2) or (X3),    wherein the imaging device is a hand-held device.-   Embodiment (X7). Imaging system (200) according to any one of claims    1 to 3 and embodiments (X2) to (X4), wherein    -   the imaging system (200) is configured for generating the        measure of authenticity after the image sensor arrangement (60)        has, in a plurality of illumination periods (t₁, t₂, . . . ,        t_(n)), imaged the non-dispersed part (40) and the dispersed        part (50); and    -   generating the measure of authenticity comprises:        -   generating, for each illumination period (t_(i)), an            intermediate measure of authenticity (k_(i)) depending at            least on a relation between the dispersed part imaged at the            illumination period (t_(i)), the non-dispersed part imaged            at the illumination period (t_(i)), and a part of the            reference spectral information, said part of the reference            spectral information being associated with how the object            (10) has been illuminated during the illumination period            (t_(i)); and        -   generating the measure of authenticity (m) based on the            plurality of generated intermediate measures of authenticity            (k₁, k₂, . . . , k_(n)).-   Embodiment (X8). Imaging system (200) of embodiment (X7), wherein    generating, for each illumination period (t_(i)), the intermediate    measure of authenticity (k_(i)) comprises:    -   determining, for each illumination period (t_(i)), the extent to        which the dispersed part imaged at the illumination period        (t_(i)) corresponds to a convolution of the non-dispersed part        imaged at the illumination period (t_(i)) and said part of the        reference spectral information associated with how the object        (10) has been illuminated during the illumination period        (t_(i)).-   Embodiment (X9). Imaging system (200) according to any one of claims    1 to 3 and embodiments (X2) to (X4), wherein    -   the imaging system (200) is configured for generating the        measure of authenticity after the image sensor arrangement (60)        has, in a plurality of illumination periods (t₁, t₂, . . . ,        t_(n)), imaged the non-dispersed part (40) and the dispersed        part (50); and    -   generating the measure of authenticity comprises:        -   processing the imaged non-dispersed part based at least on            the non-dispersed part imaged at a first illumination period            (t₁) among the plurality of illumination periods (t₁, t₂, .            . . , t_(n)) and the non-dispersed part imaged at a second            illumination period (t₂) among the plurality of illumination            periods (t₁, t₂, . . . , t_(n)), wherein the illumination            conditions during the first illumination period (t₁) at            least partially differ from the illumination conditions            during the second illumination period (t₂);        -   processing the imaged dispersed part based at least on the            dispersed part imaged at the first illumination period (t₁)            and the dispersed part imaged at the second illumination            period (t₂); and        -   generating the measure of authenticity (m) depending at            least on a relation between the processed imaged dispersed            part (A_(x)), the processed imaged non-dispersed part            (B_(x)), and the reference spectral information.-   Embodiment (X10). Imaging system (200) of embodiment (X9), wherein    generating the measure of authenticity (m) depends at least on the    extent to which the processed imaged dispersed part (A_(x))    corresponds to a convolution of the processed imaged non-dispersed    part (B_(x)) and the reference spectral information.-   Embodiment (X11). Imaging system (200) according to any one of    claims 1 to 3 and embodiments (X2) to (X4) and (X7) to (X10),    wherein the dispersive imaging arrangement (30) is positioned    relative to the image sensor arrangement (60) in such a manner as to    allow the image sensor arrangement (60) to image the non-dispersed    part (40) and the dispersed part (50) in two portions of the same    image sensor.-   Embodiment (X13). Imaging system (200) according to any one of    claims 1 to 4 and embodiments (X2) to (X4) and (X7) to (X11),    wherein a slit is not used between the dispersive imaging    arrangement (30) and the object (10) to be imaged.-   Embodiment (X17). Imaging system (200) of claim 7, wherein the at    least one machine readable code comprises at least one of a linear    barcode and a matrix barcode.-   Embodiment (X18). Imaging system (200) according to any one of    claims 5 to 7 and embodiment (X17), wherein the marking (11)    comprises single spectral characteristics at least over one region    of the marking (11).-   Embodiment (X19). Imaging system (200) of embodiment (X18), wherein    the marking (11) comprises single spectral characteristics over the    whole marking (11).-   Embodiment (X20). Imaging system (200) according to any one of    claims 5 to 7 and embodiments (X17) to (X19), wherein the marking    (11) comprises at least one of: optical agents producing specific    reflective properties upon controlled illumination, and optical    agents producing luminescence upon controlled illumination.-   Embodiment (X21). System (220) comprising an imaging system (200)    according to any one of claims 1 to 7 and embodiments (X2) to (X4),    (X7) to (X11), (X13), and (X17) to (X20), and an illumination    arrangement (210) for controlled illumination of the object (10).-   Embodiment (X23). Imaging method of claim 8, wherein the imaging    method is carried out by an imaging device.-   Embodiment (X24). Imaging method of claim 8, wherein the imaging    method is carried out by an imaging system (200) comprising an    imaging device (100) comprising the image sensor arrangement (60)    and the dispersive imaging arrangement (30), wherein the imaging    device (100) does not generate (s400) the measure of authenticity.-   Embodiment (X25). Imaging method of embodiments (X23) or (X24),    wherein the imaging device is a hand-held device.-   Embodiment (X32). Imaging method according to any one of claims 8 to    14 and embodiments (X23) to (X25), wherein the dispersive imaging    arrangement (30) is positioned relative to the image sensor    arrangement (60) in such a manner as to allow the image sensor    arrangement (60) to image the non-dispersed part (40) and the    dispersed part (50) in two portions of the same image sensor.-   Embodiment (X33). Imaging method according to any one of claims 8 to    14 and embodiments (X23) to (X25) and (X32), wherein the dispersive    imaging arrangement (30) comprises at least one of:    -   a diffractive element,    -   a transmission diffraction grating,    -   a blazed transmission diffraction grating,    -   a volume holographic grating,    -   a reflective diffraction grating,

an arrangement comprising a beam splitter and a diffraction grating, and

-   -   an arrangement comprising a beam splitter and a dispersive        prism.

-   Embodiment (X34). Imaging method according to any one of claims 8 to    14 and embodiments (X23) to (X25), (X32) and (X33), wherein a slit    is not used between the dispersive imaging arrangement (30) and the    object (10) to be imaged.

-   Embodiment (X37). Imaging method of claim 15 or 16, wherein the    marking (11) comprises at least one machine readable code.

-   Embodiment (X38). Imaging method of embodiment (X37), wherein the at    least one machine readable code comprises at least one of a linear    barcode and a matrix barcode.

-   Embodiment (X39). Imaging method according to any one of claims 15    and 16 and embodiments (X37) and (X38), wherein the marking (11)    comprises single spectral characteristics at least over one region    of the marking (11).

-   Embodiment (X40). Imaging method of embodiment (X39), wherein the    marking (11) comprises single spectral characteristics over the    whole marking (11).

-   Embodiment (X41). Imaging method according to any one of claims 15    and 16 and embodiments (X37) and (X40), wherein the marking (11)    comprises at least one of: optical agents producing specific    reflective properties upon controlled illumination, and optical    agents producing luminescence upon controlled illumination.

-   Embodiment (X43). Computer program or set of computer programs    comprising computer-executable instructions configured, when    executed a computer or set of computers, to carry out an imaging    method according to any one of claims 8 to 16 and embodiments (X23)    to (X25), (X32) to (X34), and (X37) to (X41).

-   Embodiment (X44). Computer program product or set of computer    program products comprising a computer program or set of computer    programs according to embodiment (X43).

-   Embodiment (X45). Storage medium storing a computer program or set    of computer programs according to embodiment (X43).

Where the terms “processing unit”, “storage unit”, etc. are usedherewith, no restriction is made regarding how distributed theseelements may be and regarding how gathered elements may be. That is, theconstituent elements of a unit may be distributed in different softwareor hardware components or devices for bringing about the intendedfunction. A plurality of distinct elements may also be gathered forproviding the intended functionalities.

Any one of the above-referred units, such as for example processing unit70, or devices, such as for example imaging device 110, may beimplemented in hardware, software, field-programmable gate array (FPGA),application-specific integrated circuit (ASICs), firmware or the like.

In further embodiments of the invention, any one of the above-mentionedprocessing unit, storage unit, etc. is replaced by processing means,storage means, etc. or processing module, storage module, etc.respectively, for performing the functions of the processing unit,storage unit, etc.

In further embodiments of the invention, any one of the above-describedprocedures, steps or processes may be implemented usingcomputer-executable instructions, for example in the form ofcomputer-executable procedures, methods or the like, in any kind ofcomputer languages, and/or in the form of embedded software on firmware,integrated circuits or the like.

Although the present invention has been described on the basis ofdetailed examples, the detailed examples only serve to provide theskilled person with a better understanding, and are not intended tolimit the scope of the invention. The scope of the invention is muchrather defined by the appended claims.

-   Abbreviations:-   ASICs application-specific integrated circuit-   a.u. arbitrary units-   CASSI coded aperture snapshot spectral imager-   CCD charge-coupled device-   CMOS complementary metal-oxide-semiconductor-   CTIS computed tomography imaging spectrometer-   DIBS differential illumination background subtraction-   FOV field of view-   FPGA field-programmable gate array-   KNN K-nearest neighbors algorithm-   I/mm lines per mm-   LED light-emitting diode-   LTI linear translation-invariant-   MAFC multi-aperture filtered camera-   MIFTS multiple-image Fourier transform spectrometer-   NIR near-infrared-   RAM random-access memory-   ROM read-only memory-   SHIFT snapshot hyperspectral imaging Fourier transform spectrometer-   SVM support vector machine-   SWIR short-wavelength infrared-   UV ultraviolet-   WLAN wireless local area network

The invention claimed is:
 1. An imaging system for imaging an object andgenerating a measure of authenticity of the object, the imaging systemcomprising: an image sensor arrangement having one or more imagesensors; and a dispersive imaging arrangement having one or more opticalelements, wherein the dispersive imaging arrangement is so that, whenelectromagnetic radiation from the object illuminates the dispersiveimaging arrangement, at least part of the electromagnetic radiationsplits out in different directions into at least a non-dispersed partand a dispersed part; and positioned relative to the image sensorarrangement in such a manner as to allow the image sensor arrangement toimage said non-dispersed part in a first portion of the image sensorarrangement, so as to obtain a non-dispersed image, and said dispersedpart in a second portion of the image sensor arrangement, so as toobtain a dispersed image; the imaging system being configured for, afterthe image sensor arrangement has, in at least one imaging period, imagedthe non-dispersed part and the dispersed part, generating a measure ofauthenticity of the object depending at least on a relation between theimaged dispersed part, the imaged non-dispersed part, and referencespectral information, wherein the generating of the measure ofauthenticity comprises one of: computing a synthetic image bydeconvolving the imaged dispersed part by the imaged non-dispersed part,and determining the extent to which the synthetic image corresponds tothe reference spectral information; computing a synthetic image bydeconvolving the imaged dispersed part by the reference spectralinformation, and determining the extent to which the synthetic imagecorresponds to the imaged non-dispersed part; and computing a syntheticimage by convolving the imaged non-dispersed part and the referencespectral information, and determining the extent to which the syntheticimage corresponds to the imaged dispersed part.
 2. The imaging systemaccording to claim 1, wherein the dispersive imaging arrangementcomprises at least one of: a diffractive element, a transmissiondiffraction grating, a blazed transmission diffraction grating, a volumeholographic grating, a reflective diffraction grating, an arrangementcomprising a beam splitter and a diffraction grating, and an arrangementcomprising a beam splitter and a dispersive prism.
 3. The imaging systemaccording to claim 1, for imaging an object bearing a marking.
 4. Theimaging system of claim 3, wherein generating the measure ofauthenticity further comprises decoding a code from the marking withinthe imaged non-dispersed part and verifying the authenticity of thecode.
 5. The imaging system of claim 3, wherein the marking comprises atleast one machine readable code.
 6. An imaging method for imaging anobject and generating a measure of authenticity of the object, theimaging method making use of: an image sensor arrangement having one ormore image sensors; and a dispersive imaging arrangement having one ormore optical elements, wherein the dispersive imaging arrangement is sothat, when electromagnetic radiation from the object illuminates thedispersive imaging arrangement, at least part of the electromagneticradiation splits out in different directions into at least anon-dispersed part and a dispersed part; and positioned relative to theimage sensor arrangement in such a manner as to allow the image sensorarrangement to image said non-dispersed part in a first portion of theimage sensor arrangement, so as to obtain a non-dispersed image, andsaid dispersed part in a second portion of the image sensor arrangement,so as to obtain a non-dispersed image; and the imaging methodcomprising: imaging, by the image sensor arrangement, in at least oneimaging period, the non-dispersed part and the dispersed part, andgenerating a measure of authenticity of the object depending at least ona relation between the imaged dispersed part, the imaged non-dispersedpart, and reference spectral information, wherein the generating of themeasure of authenticity comprises one of: computing a synthetic image bydeconvolving the imaged dispersed part by the imaged non-dispersed part,and determining the extent to which the synthetic image corresponds tothe reference spectral information; computing a synthetic image bydeconvolving the imaged dispersed part by the reference spectralinformation, and determining the extent to which the synthetic imagecorresponds to the imaged non-dispersed part; and computing a syntheticimage by convolving the imaged non-dispersed part and the referencespectral information, and determining the extent to which the syntheticimage corresponds to the imaged dispersed part.
 7. The imaging methodaccording to claim 6, comprising imaging, by the image sensorarrangement, in a plurality of illumination periods, the non-dispersedpart and the dispersed part, wherein generating the measure ofauthenticity comprises: generating, for each illumination period, anintermediate measure of authenticity depending at least on a relationbetween the dispersed part imaged at the illumination period, the imagednon-dispersed part at the illumination period, and a part of thereference spectral information, said part of the reference spectralinformation being associated with how the object has been illuminatedduring the illumination period; and generating the measure ofauthenticity based on the plurality of generated intermediate measuresof authenticity.
 8. The imaging method according to claim 6, comprisingimaging, by the image sensor arrangement, in a plurality of illuminationperiods, the non-dispersed part and the dispersed part, whereingenerating the measure of authenticity comprises: processing the imagednon-dispersed part based at least on the non-dispersed part imaged at afirst illumination period among the plurality of illumination periodsand the non-dispersed part imaged at a second illumination period amongthe plurality of illumination periods, wherein the illuminationconditions during the first illumination period at least partiallydiffer from the illumination conditions during the second illuminationperiod; processing the imaged dispersed part based at least on thedispersed part imaged at the first illumination period and the dispersedpart imaged at the second illumination period; and generating themeasure of authenticity depending at least on a relation between theprocessed imaged dispersed part, the processed imaged non-dispersedpart, and the reference spectral information.
 9. The imaging methodaccording to claim 6, for imaging an object bearing a marking.
 10. Theimaging method of claim 9, wherein generating the measure ofauthenticity further comprises decoding a code from the marking withinthe imaged non-dispersed part and verifying the authenticity of thecode.
 11. The imaging method according to claim 6, further comprising astep of controlled illumination of the object.